{"id":468999,"date":"2018-02-23T09:52:00","date_gmt":"2018-02-23T17:52:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=468999"},"modified":"2018-10-16T22:29:26","modified_gmt":"2018-10-17T05:29:26","slug":"genetic-programming-experiment-natural-language-grammar-engineering","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/genetic-programming-experiment-natural-language-grammar-engineering\/","title":{"rendered":"A Genetic Programming Experiment in Natural Language Grammar Engineering"},"content":{"rendered":"

This paper describes an experiment in grammar engineering for a\u00a0shallow syntactic parser using Genetic Programming and a\u00a0treebank. The goal of the experiment is to improve the Parseval<\/em> score of a\u00a0previously manually created seed grammar. We illustrate the adaptation of the Genetic Programming paradigm to the problem of grammar engineering. The used genetic operators are described. The performance of the evolved grammar after 1,000 generations on an unseen test set is improved by 2.7 points F-score (3.7 points on the training set). Despite the large number of generations no overfitting effect is observed.<\/p>\n","protected":false},"excerpt":{"rendered":"

This paper describes an experiment in grammar engineering for a\u00a0shallow syntactic parser using Genetic Programming and a\u00a0treebank. The goal of the experiment is to improve the Parseval score of a\u00a0previously manually created seed grammar. We illustrate the adaptation of the Genetic Programming paradigm to the problem of grammar engineering. The used genetic operators are described. 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